论文信息 - A-Learning for approximate planning

A-Learning for approximate planning

Abstract We consider a new algorithm for reinforcement learning called A-learning. A-learning learns the advantages from a single training set. We compare A-learning with function approximation to Q-learning with function approximation and find that because A-learning approximates only the advantages it is less likely to exhibit bias due to the function approximation as compared to Q-learning.We consider a new algorithm for reinforcement learning called A-learning. A-learning learns the advantages from a single training set. We compare A-learning with function approximation to Q-learning with function approximation and find that because A-learning approximates only the advantages it is less likely to exhibit bias due to the function approximation as compared to Q-learning.

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